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Should arrests be allowed on the sole basis of “facial recognition and biometric information”? Democrat Assembly Member Linda Rosenthal doesn’t think so. She recently introduced Bill A00768, a document that would prohibit biometric identifying technology being the sole factor to place an individual in custody.
Biometric data in this case includes handprints, irises and voice biometrics as well as facial recognition data. The debate around biometric data continues to be a hot topic. Many believe that this data can be used to improve law enforcement, while others point to situations from the past to demonstrate its danger.
In the UK, there has been a push toward allowing the police to use facial recognition.
Last year, a trial project for the South Wales Police to scan thousands of faces was ruled unlawful. The Welsh company Credas has come out in opposition to this law, accusing the government of not keeping up with the latest technology.
“Ten years ago it would have felt space age, but now it’s everywhere—just logging into my phone or laptop, we’re all used to it now. But the legislation will never keep up with the technological advancements,” said Chief Executive Officer of Credas Rhys David.
David believes that face biometrics are helping his company reduce fraud in the real estate sector. Credas has selected ID R&D to provide biometrics and liveness checking. ID R&D hopes to help Credas customers meet anti-money laundering and right to work regulatory requirements through its biometric technology.
Credas is not alone in their perspective. A recent study done by Pew Research Center showed that 56% of Americans trust law enforcement use of facial recognition and 59% are accepting of its use to assess threats in public spaces. This public push toward using biometric technology is indicative of a larger trend showcasing the growing importance of this discussion around how and when biometric data should be used.
In the past, however, there have been incidents that indicate the danger of becoming increasingly reliant on biometric data.
For example, a NIST study found that Asian, African American and Native American groups have higher false-positive rates than caucasian groups when facial recognition technology is used. This inaccuracy in a law enforcement setting would mean that there are a greater percentage of false arrests for these racial groups. Given that there is already evidence that law enforcement treats people of color unfairly, this inaccuracy has the potential to further increase inequalities and become quite dangerous.
Additionally, there have already been issues with law enforcement agencies using Clearview AI facial recognition technology and giving the startup access to a growing database of individuals who have attracted attention from law enforcement. They did all of this without getting consent from the individuals or federal permission.
All this not only unveils a deep rooted issue with biometric technology, but also shows that governments are far from immune to misusing facial recognition technology. It shows the importance of there being proper precautions, such as the bill introduced in New York, to ensure biometric information isn’t used harmfully.
The law introduced by Rosenthal would require all state and local law enforcement agencies to craft a written policy barring them from “stopping, detention or search of any person” on the sole grounds of biometric match. The bill has been referred to the Assembly’s Governmental Relations Committee.
The implications of this law expand far beyond its future in the New York state government. The increased usage and continued innovation of biometric technology is inevitable, and how biometric information will be used continues to be pivotal for the future of tech and data ethics. This bill brings into question how accurate biometric data is and how much power it should hold, and will undoubtedly add to the conversations around this growing technology.
ABOUT THE WRITER
Sarah Raza is a Tech Innovation Fellow with a background in computer science from Stanford. She is passionate about exploring the implications of increased usage of artificial intelligence and machine learning.
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